def compute_connectivity_and_shape_indexes(self):
"""compute the compute connectivity and shape indexes.
Ref: Rev. Comput. Chem. 2:367-422 (1991)
Returns:
CSI_dict: CSI dictionary, data type: float
"""
assert type(self.Molecule) == Chem.rdchem.Mol
CSI_dict = {}
CSI_dict['Chi0v'] = rdDesc.CalcChi0v(self.Molecule)
CSI_dict['Chi1v'] = rdDesc.CalcChi1v(self.Molecule)
CSI_dict['Chi2v'] = rdDesc.CalcChi2v(self.Molecule)
CSI_dict['Chi3v'] = rdDesc.CalcChi3v(self.Molecule)
CSI_dict['Chi4v'] = rdDesc.CalcChi4v(self.Molecule)
CSI_dict['Chi0n'] = rdDesc.CalcChi0n(self.Molecule)
CSI_dict['Chi1n'] = rdDesc.CalcChi1n(self.Molecule)
CSI_dict['Chi2n'] = rdDesc.CalcChi2n(self.Molecule)
CSI_dict['Chi3n'] = rdDesc.CalcChi3n(self.Molecule)
CSI_dict['Chi4n'] = rdDesc.CalcChi4n(self.Molecule)
CSI_dict['HallKierAlpha'] = rdDesc.CalcHallKierAlpha(self.Molecule)
CSI_dict['Kappa1'] = rdDesc.CalcKappa1(self.Molecule)
CSI_dict['Kappa2'] = rdDesc.CalcKappa2(self.Molecule)
CSI_dict['Kappa3'] = rdDesc.CalcKappa3(self.Molecule)
return CSI_dict
def _calculateDescriptors(mol):
df = pd.DataFrame(index=[0])
df["SlogP"] = rdMolDescriptors.CalcCrippenDescriptors(mol)[0]
df["SMR"] = rdMolDescriptors.CalcCrippenDescriptors(mol)[1]
df["LabuteASA"] = rdMolDescriptors.CalcLabuteASA(mol)
df["TPSA"] = Descriptors.TPSA(mol)
df["AMW"] = Descriptors.MolWt(mol)
df["ExactMW"] = rdMolDescriptors.CalcExactMolWt(mol)
df["NumLipinskiHBA"] = rdMolDescriptors.CalcNumLipinskiHBA(mol)
df["NumLipinskiHBD"] = rdMolDescriptors.CalcNumLipinskiHBD(mol)
df["NumRotatableBonds"] = rdMolDescriptors.CalcNumRotatableBonds(mol)
df["NumHBD"] = rdMolDescriptors.CalcNumHBD(mol)
df["NumHBA"] = rdMolDescriptors.CalcNumHBA(mol)
df["NumAmideBonds"] = rdMolDescriptors.CalcNumAmideBonds(mol)
df["NumHeteroAtoms"] = rdMolDescriptors.CalcNumHeteroatoms(mol)
df["NumHeavyAtoms"] = Chem.rdchem.Mol.GetNumHeavyAtoms(mol)
df["NumAtoms"] = Chem.rdchem.Mol.GetNumAtoms(mol)
df["NumRings"] = rdMolDescriptors.CalcNumRings(mol)
df["NumAromaticRings"] = rdMolDescriptors.CalcNumAromaticRings(mol)
df["NumSaturatedRings"] = rdMolDescriptors.CalcNumSaturatedRings(mol)
df["NumAliphaticRings"] = rdMolDescriptors.CalcNumAliphaticRings(mol)
df["NumAromaticHeterocycles"] = \
rdMolDescriptors.CalcNumAromaticHeterocycles(mol)
df["NumSaturatedHeterocycles"] = \
rdMolDescriptors.CalcNumSaturatedHeterocycles(mol)
df["NumAliphaticHeterocycles"] = \
rdMolDescriptors.CalcNumAliphaticHeterocycles(mol)
df["NumAromaticCarbocycles"] = \
rdMolDescriptors.CalcNumAromaticCarbocycles(mol)
df["NumSaturatedCarbocycles"] = \
rdMolDescriptors.CalcNumSaturatedCarbocycles(mol)
df["NumAliphaticCarbocycles"] = \
rdMolDescriptors.CalcNumAliphaticCarbocycles(mol)
df["FractionCSP3"] = rdMolDescriptors.CalcFractionCSP3(mol)
df["Chi0v"] = rdMolDescriptors.CalcChi0v(mol)
df["Chi1v"] = rdMolDescriptors.CalcChi1v(mol)
df["Chi2v"] = rdMolDescriptors.CalcChi2v(mol)
df["Chi3v"] = rdMolDescriptors.CalcChi3v(mol)
df["Chi4v"] = rdMolDescriptors.CalcChi4v(mol)
df["Chi1n"] = rdMolDescriptors.CalcChi1n(mol)
df["Chi2n"] = rdMolDescriptors.CalcChi2n(mol)
df["Chi3n"] = rdMolDescriptors.CalcChi3n(mol)
df["Chi4n"] = rdMolDescriptors.CalcChi4n(mol)
df["HallKierAlpha"] = rdMolDescriptors.CalcHallKierAlpha(mol)
df["kappa1"] = rdMolDescriptors.CalcKappa1(mol)
df["kappa2"] = rdMolDescriptors.CalcKappa2(mol)
df["kappa3"] = rdMolDescriptors.CalcKappa3(mol)
slogp_VSA = list(map(lambda i: "slogp_VSA" + str(i), list(range(1, 13))))
df = df.assign(**dict(zip(slogp_VSA, rdMolDescriptors.SlogP_VSA_(mol))))
smr_VSA = list(map(lambda i: "smr_VSA" + str(i), list(range(1, 11))))
df = df.assign(**dict(zip(smr_VSA, rdMolDescriptors.SMR_VSA_(mol))))
peoe_VSA = list(map(lambda i: "peoe_VSA" + str(i), list(range(1, 15))))
df = df.assign(**dict(zip(peoe_VSA, rdMolDescriptors.PEOE_VSA_(mol))))
MQNs = list(map(lambda i: "MQN" + str(i), list(range(1, 43))))
df = df.assign(**dict(zip(MQNs, rdMolDescriptors.MQNs_(mol))))
return df
def _pyChi4n(mol):
""" Similar to Hall Kier Chi4v, but uses nVal instead of valence
This makes a big difference after we get out of the first row.
**NOTE**: because the current path finding code does, by design,
detect rings as paths (e.g. in C1CC1 there is *1* atom path of
length 3), values of Chi4n may give results that differ from those
provided by the old code in molecules that have 3 rings.
"""
return _pyChiNn_(mol, 4)
Chi0v = lambda x: rdMolDescriptors.CalcChi0v(x)
Chi0v.version = rdMolDescriptors._CalcChi0v_version
Chi1v = lambda x: rdMolDescriptors.CalcChi1v(x)
Chi1v.version = rdMolDescriptors._CalcChi1v_version
Chi2v = lambda x: rdMolDescriptors.CalcChi2v(x)
Chi2v.version = rdMolDescriptors._CalcChi2v_version
Chi3v = lambda x: rdMolDescriptors.CalcChi3v(x)
Chi3v.version = rdMolDescriptors._CalcChi3v_version
Chi4v = lambda x: rdMolDescriptors.CalcChi4v(x)
Chi4v.version = rdMolDescriptors._CalcChi4v_version
ChiNv_ = lambda x, y: rdMolDescriptors.CalcChiNv(x, y)
ChiNv_.version = rdMolDescriptors._CalcChiNv_version
Chi0n = lambda x: rdMolDescriptors.CalcChi0n(x)
Chi0n.version = rdMolDescriptors._CalcChi0n_version
Chi1n = lambda x: rdMolDescriptors.CalcChi1n(x)
def get_global_features(self, mol):
u = []
# Now get some specific features
fdefName = os.path.join(RDConfig.RDDataDir, 'BaseFeatures.fdef')
factory = ChemicalFeatures.BuildFeatureFactory(fdefName)
feats = factory.GetFeaturesForMol(mol)
# First get some basic features
natoms = mol.GetNumAtoms()
nbonds = mol.GetNumBonds()
mw = Descriptors.ExactMolWt(mol)
HeavyAtomMolWt = Descriptors.HeavyAtomMolWt(mol)
NumValenceElectrons = Descriptors.NumValenceElectrons(mol)
''' # These four descriptors are producing the value of infinity for refcode_csd = YOLJUF (CCOP(=O)(Cc1ccc(cc1)NC(=S)NP(OC(C)C)(OC(C)C)[S])OCC\t\n)
MaxAbsPartialCharge = Descriptors.MaxAbsPartialCharge(mol)
MaxPartialCharge = Descriptors.MaxPartialCharge(mol)
MinAbsPartialCharge = Descriptors.MinAbsPartialCharge(mol)
MinPartialCharge = Descriptors.MinPartialCharge(mol)
'''
# FpDensityMorgan1 = Descriptors.FpDensityMorgan1(mol)
# FpDensityMorgan2 = Descriptors.FpDensityMorgan2(mol)
# FpDensityMorgan3 = Descriptors.FpDensityMorgan3(mol)
# Get some features using chemical feature factory
nbrAcceptor = 0
nbrDonor = 0
nbrHydrophobe = 0
nbrLumpedHydrophobe = 0
nbrPosIonizable = 0
nbrNegIonizable = 0
for j in range(len(feats)):
#print(feats[j].GetFamily(), feats[j].GetType())
if ('Acceptor' == (feats[j].GetFamily())):
nbrAcceptor = nbrAcceptor + 1
elif ('Donor' == (feats[j].GetFamily())):
nbrDonor = nbrDonor + 1
elif ('Hydrophobe' == (feats[j].GetFamily())):
nbrHydrophobe = nbrHydrophobe + 1
elif ('LumpedHydrophobe' == (feats[j].GetFamily())):
nbrLumpedHydrophobe = nbrLumpedHydrophobe + 1
elif ('PosIonizable' == (feats[j].GetFamily())):
nbrPosIonizable = nbrPosIonizable + 1
elif ('NegIonizable' == (feats[j].GetFamily())):
nbrNegIonizable = nbrNegIonizable + 1
else:
pass
#print(feats[j].GetFamily())
# Now get some features using rdMolDescriptors
moreGlobalFeatures = [rdm.CalcNumRotatableBonds(mol), rdm.CalcChi0n(mol), rdm.CalcChi0v(mol), \
rdm.CalcChi1n(mol), rdm.CalcChi1v(mol), rdm.CalcChi2n(mol), rdm.CalcChi2v(mol), \
rdm.CalcChi3n(mol), rdm.CalcChi4n(mol), rdm.CalcChi4v(mol), \
rdm.CalcFractionCSP3(mol), rdm.CalcHallKierAlpha(mol), rdm.CalcKappa1(mol), \
rdm.CalcKappa2(mol), rdm.CalcLabuteASA(mol), \
rdm.CalcNumAliphaticCarbocycles(mol), rdm.CalcNumAliphaticHeterocycles(mol), \
rdm.CalcNumAliphaticRings(mol), rdm.CalcNumAmideBonds(mol), \
rdm.CalcNumAromaticCarbocycles(mol), rdm.CalcNumAromaticHeterocycles(mol), \
rdm.CalcNumAromaticRings(mol), rdm.CalcNumBridgeheadAtoms(mol), rdm.CalcNumHBA(mol), \
rdm.CalcNumHBD(mol), rdm.CalcNumHeteroatoms(mol), rdm.CalcNumHeterocycles(mol), \
rdm.CalcNumLipinskiHBA(mol), rdm.CalcNumLipinskiHBD(mol), rdm.CalcNumRings(mol), \
rdm.CalcNumSaturatedCarbocycles(mol), rdm.CalcNumSaturatedHeterocycles(mol), \
rdm.CalcNumSaturatedRings(mol), rdm.CalcNumSpiroAtoms(mol), rdm.CalcTPSA(mol)]
u = [natoms, nbonds, mw, HeavyAtomMolWt, NumValenceElectrons, \
nbrAcceptor, nbrDonor, nbrHydrophobe, nbrLumpedHydrophobe, \
nbrPosIonizable, nbrNegIonizable]
u = u + moreGlobalFeatures
u = np.array(u).T
# Some of the descriptors produice NAN. We can convert them to 0
# If you are getting outliers in the training or validation set this could be
# Because some important features were set to zero here because it produced NAN
# Removing those features from the feature set might remove the outliers
#u[np.isnan(u)] = 0
#u = torch.tensor(u, dtype=torch.float)
return (u)
def get_molecular_features(dataframe, mol_list):
df = dataframe
for i in range(len(mol_list)):
print("Getting molecular features for molecule: ", i)
mol = mol_list[i]
natoms = mol.GetNumAtoms()
nbonds = mol.GetNumBonds()
mw = Descriptors.ExactMolWt(mol)
df.at[i,"NbrAtoms"] = natoms
df.at[i,"NbrBonds"] = nbonds
df.at[i,"mw"] = mw
df.at[i,'HeavyAtomMolWt'] = Chem.Descriptors.HeavyAtomMolWt(mol)
df.at[i,'NumValenceElectrons'] = Chem.Descriptors.NumValenceElectrons(mol)
''' # These four descriptors are producing the value of infinity for refcode_csd = YOLJUF (CCOP(=O)(Cc1ccc(cc1)NC(=S)NP(OC(C)C)(OC(C)C)[S])OCC\t\n)
df.at[i,'MaxAbsPartialCharge'] = Chem.Descriptors.MaxAbsPartialCharge(mol)
df.at[i,'MaxPartialCharge'] = Chem.Descriptors.MaxPartialCharge(mol)
df.at[i,'MinAbsPartialCharge'] = Chem.Descriptors.MinAbsPartialCharge(mol)
df.at[i,'MinPartialCharge'] = Chem.Descriptors.MinPartialCharge(mol)
'''
df.at[i,'FpDensityMorgan1'] = Chem.Descriptors.FpDensityMorgan1(mol)
df.at[i,'FpDensityMorgan2'] = Chem.Descriptors.FpDensityMorgan2(mol)
df.at[i,'FpDensityMorgan3'] = Chem.Descriptors.FpDensityMorgan3(mol)
#print(natoms, nbonds)
# Now get some specific features
fdefName = os.path.join(RDConfig.RDDataDir,'BaseFeatures.fdef')
factory = ChemicalFeatures.BuildFeatureFactory(fdefName)
feats = factory.GetFeaturesForMol(mol)
#df["Acceptor"] = 0
#df["Aromatic"] = 0
#df["Hydrophobe"] = 0
nbrAcceptor = 0
nbrDonor = 0
nbrHydrophobe = 0
nbrLumpedHydrophobe = 0
nbrPosIonizable = 0
nbrNegIonizable = 0
for j in range(len(feats)):
#print(feats[j].GetFamily(), feats[j].GetType())
if ('Acceptor' == (feats[j].GetFamily())):
nbrAcceptor = nbrAcceptor + 1
elif ('Donor' == (feats[j].GetFamily())):
nbrDonor = nbrDonor + 1
elif ('Hydrophobe' == (feats[j].GetFamily())):
nbrHydrophobe = nbrHydrophobe + 1
elif ('LumpedHydrophobe' == (feats[j].GetFamily())):
nbrLumpedHydrophobe = nbrLumpedHydrophobe + 1
elif ('PosIonizable' == (feats[j].GetFamily())):
nbrPosIonizable = nbrPosIonizable + 1
elif ('NegIonizable' == (feats[j].GetFamily())):
nbrNegIonizable = nbrNegIonizable + 1
else:
pass#print(feats[j].GetFamily())
df.at[i,"Acceptor"] = nbrAcceptor
df.at[i,"Donor"] = nbrDonor
df.at[i,"Hydrophobe"] = nbrHydrophobe
df.at[i,"LumpedHydrophobe"] = nbrLumpedHydrophobe
df.at[i,"PosIonizable"] = nbrPosIonizable
df.at[i,"NegIonizable"] = nbrNegIonizable
# We can also get some more molecular features using rdMolDescriptors
df.at[i,"NumRotatableBonds"] = rdMolDescriptors.CalcNumRotatableBonds(mol)
df.at[i,"CalcChi0n"] = rdMolDescriptors.CalcChi0n(mol)
df.at[i,"CalcChi0v"] = rdMolDescriptors.CalcChi0v(mol)
df.at[i,"CalcChi1n"] = rdMolDescriptors.CalcChi1n(mol)
df.at[i,"CalcChi1v"] = rdMolDescriptors.CalcChi1v(mol)
df.at[i,"CalcChi2n"] = rdMolDescriptors.CalcChi2n(mol)
df.at[i,"CalcChi2v"] = rdMolDescriptors.CalcChi2v(mol)
df.at[i,"CalcChi3n"] = rdMolDescriptors.CalcChi3n(mol)
df.at[i,"CalcChi3v"] = rdMolDescriptors.CalcChi3v(mol)
df.at[i,"CalcChi4n"] = rdMolDescriptors.CalcChi4n(mol)
df.at[i,"CalcChi4v"] = rdMolDescriptors.CalcChi4v(mol)
df.at[i,"CalcFractionCSP3"] = rdMolDescriptors.CalcFractionCSP3(mol)
df.at[i,"CalcHallKierAlpha"] = rdMolDescriptors.CalcHallKierAlpha(mol)
df.at[i,"CalcKappa1"] = rdMolDescriptors.CalcKappa1(mol)
df.at[i,"CalcKappa2"] = rdMolDescriptors.CalcKappa2(mol)
#df.at[i,"CalcKappa3"] = rdMolDescriptors.CalcKappa3(mol)
df.at[i,"CalcLabuteASA"] = rdMolDescriptors.CalcLabuteASA(mol)
df.at[i,"CalcNumAliphaticCarbocycles"] = rdMolDescriptors.CalcNumAliphaticCarbocycles(mol)
df.at[i,"CalcNumAliphaticHeterocycles"] = rdMolDescriptors.CalcNumAliphaticHeterocycles(mol)
df.at[i,"CalcNumAliphaticRings"] = rdMolDescriptors.CalcNumAliphaticRings(mol)
df.at[i,"CalcNumAmideBonds"] = rdMolDescriptors.CalcNumAmideBonds(mol)
df.at[i,"CalcNumAromaticCarbocycles"] = rdMolDescriptors.CalcNumAromaticCarbocycles(mol)
df.at[i,"CalcNumAromaticHeterocycles"] = rdMolDescriptors.CalcNumAromaticHeterocycles(mol)
df.at[i,"CalcNumAromaticRings"] = rdMolDescriptors.CalcNumAromaticRings(mol)
df.at[i,"CalcNumBridgeheadAtoms"] = rdMolDescriptors.CalcNumBridgeheadAtoms(mol)
df.at[i,"CalcNumHBA"] = rdMolDescriptors.CalcNumHBA(mol)
df.at[i,"CalcNumHBD"] = rdMolDescriptors.CalcNumHBD(mol)
df.at[i,"CalcNumHeteroatoms"] = rdMolDescriptors.CalcNumHeteroatoms(mol)
df.at[i,"CalcNumHeterocycles"] = rdMolDescriptors.CalcNumHeterocycles(mol)
df.at[i,"CalcNumLipinskiHBA"] = rdMolDescriptors.CalcNumLipinskiHBA(mol)
df.at[i,"CalcNumLipinskiHBD"] = rdMolDescriptors.CalcNumLipinskiHBD(mol)
df.at[i,"CalcNumRings"] = rdMolDescriptors.CalcNumRings(mol)
df.at[i,"CalcNumSaturatedCarbocycles"] = rdMolDescriptors.CalcNumSaturatedCarbocycles(mol)
df.at[i,"CalcNumSaturatedHeterocycles"] = rdMolDescriptors.CalcNumSaturatedHeterocycles(mol)
df.at[i,"CalcNumSaturatedRings"] = rdMolDescriptors.CalcNumSaturatedRings(mol)
df.at[i,"CalcNumSpiroAtoms"] = rdMolDescriptors.CalcNumSpiroAtoms(mol)
df.at[i,"CalcTPSA"] = rdMolDescriptors.CalcTPSA(mol)
return(df)